Neural networks can be used to analyze images for a variety of purposes. For example, some neural networks can examine images in order to identify objects or features depicted in the images. The neural networks can be established or modified (e.g., trained) to detect various objects in images by providing the neural networks with labeled training images. The labeled training images include images having known objects depicted in the images, with each pixel in the labeled training images identified according to what object or type of object the pixel at least partially represents.
But, the process for labeling training images is a time-consuming, costly, and/or laborious process due to the number of pixels in high definition training images. While some crowd-sourcing approaches have been used to reduce the time and/or cost involved in labeling the training images, not all images are available for public dissemination for the crowd-sourcing solutions. For example, medical images can be subject to laws that restrict dissemination of the images, images of certain objects (e.g., airplane engines) may not be open to public dissemination due to contractual and/or governmental restrictions, other images may be subject to privacy laws that restrict public dissemination, etc.